Every PE-backed company has an exit readiness checklist. Financial statements are audited. Legal documents are organized. The management presentation is polished.
Then diligence starts and someone asks a data question the team cannot answer cleanly. The deal slows. Adjustments appear. The multiple gets discussed again.
This is not a hypothetical. According to EY, 72% of PE firms cite data and KPIs as their biggest exit challenge. An Accordion survey found 67% say data quality is a persistent weak spot in their portfolio companies. The problem is known. The fix is just not prioritized until it is too late.
This checklist covers what most teams miss. Not the obvious things like audited financials, but the data infrastructure underneath that determines whether your numbers hold up under scrutiny.
Why data readiness is different from exit readiness
Exit readiness is about having your house in order. Data readiness is about the plumbing.
You can have a clean house with bad plumbing. Everything looks fine until someone turns on the faucet and the pressure drops. In deal terms, everything looks fine until a buyer asks a question that requires pulling data from two systems and reconciling them.
Traditional exit checklists focus on outputs. Audited financials, legal structure, management team, customer contracts. Data readiness focuses on the inputs. Where do the numbers come from? How are they calculated? Can someone other than the person who built the spreadsheet reproduce them?
Most mid-market companies discover the gap between these two when a diligence team sends their first request list and the answers take three weeks instead of three days.
The checklist
I have organized this by area, from most commonly tested to most commonly missed. Each item includes what “good” looks like and what signals trouble.
Financial data
Revenue reconciliation across systems
Can you tie revenue from your CRM to your billing system to your GL? All three should match within a defined tolerance. If they do not, you need to understand why and document the differences.
Good: Revenue reconciles across systems within 1% with documented explanations for any variance.
Trouble: “We reconcile annually as part of the audit.” Annual reconciliation means eleven months of unexplained drift.
Monthly close process documentation
What happens between month end and when the numbers are final? Who does what? How long does it take?
Good: Documented close process. Five to seven business days. Defined responsibilities and review steps.
Trouble: “It depends on the month.” An inconsistent close process tells the buyer the numbers are assembled differently every time.
EBITDA adjustment support
Every adjustment needs a data trail. Not a note in a spreadsheet. A trail someone else can follow from the adjustment back to the underlying transaction.
Good: Adjustment schedule with supporting data, updated monthly, reviewed by the controller.
Trouble: A spreadsheet that only the CFO understands with formulas that reference other spreadsheets.
Historical trend data (36 months minimum)
Buyers want to see trends, not snapshots. Monthly granularity for revenue, gross margin, key operating metrics, and customer counts.
Good: 36 months of monthly data in a structured format, consistent definitions throughout.
Trouble: “We changed systems 18 months ago so the data before that is in a different format.” System migrations that break data continuity are common. Bridging the gap before diligence is critical.
Operational data
KPI definitions and calculation methodology
Every KPI in your board deck needs a documented definition. How is it calculated? Where does the data come from? Has the definition changed?
Good: KPI dictionary with formulas, source systems, and change log. Matches what you present to the board.
Trouble: “Gross margin” means something different in the board deck than it does in the financial statements.
Unit economics by segment
Can you show profitability by product line, service type, customer segment, or geography? Not just revenue. Actual contribution margin with allocated costs.
Good: Segment P&L with documented allocation methodology, reviewed quarterly.
Trouble: “We know overall margins are healthy.” Blended margins hide cross-subsidization. Buyers will find it.
Operational reporting cadence
What reports does the management team actually use to run the business? How often? Who produces them?
Good: Weekly operational dashboards, monthly financial review, quarterly strategic review. All automated or semi-automated with documented sources.
Trouble: “The CEO gets a report from each department head in their own format.” This tells the buyer there is no unified view of the business.
System inventory and data flows
What systems does the business run on? How do they connect? Where does data move between them?
Good: A data flow diagram showing source systems, integration points, and reporting layers. Last updated within the past six months.
Trouble: “We have not documented that.” If nobody has documented how data moves through the business, nobody fully understands the business.
Customer data
Customer segmentation and cohort analysis
Can you show how different customer groups behave over time? Retention by cohort, revenue by segment, growth by acquisition channel?
Good: Cohort analysis going back at least 24 months with consistent definitions. Segmentation by revenue tier, geography, product, and acquisition channel.
Trouble: “We can give you total customer count and total revenue.” Aggregate numbers hide the story. Buyers need to understand the composition.
Churn analysis with root causes
Not just the number. Why are customers leaving? Is churn concentrated in a segment? Is it getting better or worse?
Good: Monthly churn rate by segment with tagged reasons. Trend analysis showing whether interventions are working.
Trouble: “We track churn but do not have a systematic way to capture reasons.” Without root causes, the buyer cannot assess whether churn is structural or fixable.
NPS or customer satisfaction data
If you measure it, it will be asked for. If you do not measure it, that will be noted.
Good: Regular NPS or CSAT measurement with response rates above 20% and trend data.
Trouble: “We did a survey two years ago.” Stale satisfaction data is almost worse than no data. It suggests you stopped paying attention.
Customer concentration analysis
What percentage of revenue comes from your top 5, 10, and 20 customers? How has this changed over time?
Good: Quarterly concentration analysis showing top customer revenue share is stable or declining. No single customer above 15%.
Trouble: “Our biggest customer is 30% of revenue.” High concentration is not automatically a dealbreaker, but being unable to show a plan to diversify is.
Technology and infrastructure
Data security and PII handling
Where is personally identifiable information stored? Who has access? How is it protected?
Good: Data classification policy, PII inventory, access controls, and incident response plan. SOC 2 Type II is the gold standard but not always required.
Trouble: “We follow industry best practices” with no documentation to support the claim.
Key person dependencies in data and reporting
Who knows how to run the reports? Who built the integrations? Who understands the data model?
Good: Cross-trained team. Documentation that allows someone new to produce key reports within a week.
Trouble: “Sarah built all of that.” If Sarah leaves between signing and close, the buyer has a problem. They know this.
Technical debt inventory
Not everything needs to be fixed. But the buyer needs to know what exists, what the impact is, and what the cost to remediate would be.
Good: Known issues documented with severity, impact, and estimated cost to fix. Priority items addressed or in progress.
Trouble: “We know there are some issues but we have not catalogued them.” Unknown unknowns scare buyers more than known problems.
Backup, recovery, and business continuity
Can you recover your data if something goes wrong? Have you tested it?
Good: Documented backup procedures, tested recovery within the last 12 months, recovery time objectives defined.
Trouble: “We back up to the cloud.” Without tested recovery, a backup is a hope, not a plan.
The timeline
Data readiness is not a project you can cram into the last month before diligence. Here is a realistic timeline.
18 to 12 months before exit
Run an internal assessment. Answer every question on this checklist. Identify gaps. Prioritize based on what will have the biggest impact on deal speed and valuation.
This is the assessment phase. No remediation yet. Just an honest inventory of where you stand.
12 to 6 months before exit
Fix the critical gaps. Revenue reconciliation, KPI documentation, key person cross-training, system documentation. These are the items that will slow diligence or trigger adjustments.
Most mid-market companies can address the high-priority items in this window if they start with a clear list and dedicated resources.
6 to 3 months before exit
Polish and test. Run a mock diligence exercise. Have someone outside your core team request data and see how quickly and accurately the team responds. Fix what breaks.
For a detailed timeline breakdown with specific deliverables at each phase, read How Long Does It Take to Fix Data Before Diligence?
3 months to exit
Maintain and update. Your data readiness should be a living process at this point, not a project. Monthly updates to documentation, regular reconciliation, ongoing quality checks.
What “good enough” looks like
Perfection is not the goal. I have seen companies delay their exit because they wanted to build a data warehouse first. That is the wrong instinct.
Good enough means:
- You can answer the 15 questions buyers ask within 48 hours (here is the test)
- Your numbers reconcile across systems with documented explanations for any variance
- Key reports can be produced by more than one person
- You know where your data quality problems are and have a plan for the important ones
- Your data architecture is documented at a level that a new hire could understand it
That is Level 2 readiness. Most mid-market companies should aim for Level 2. Level 3 (investor-grade, real-time, fully automated) is nice but not necessary for a successful exit.
The goal is to remove data as a source of risk or delay in the deal process. Not to win an award for data management.
What to read next
If you are starting from scratch, begin with The Complete Data Diligence Guide for a detailed walkthrough of what buyers test and how to prepare.
If you want to understand how data issues show up in financial diligence, read Why Your QoE Report Will Surface Data Problems.
For a weekly brief on exit readiness, data diligence, and practical tools for PE-backed teams, subscribe to Inside the Data Room.